Agricultural cold chain logistics is characterized by inherent challenges-product perishability, high carbon emissions, and stringent time windows-which are further exacerbated by dynamic disruptions. Existing methods suffer from slow adaptability, unstable multi-objective convergence, and severe cold-start issues. This work falls within the broad scope of biomimetics-the science of emulating nature's time-tested strategies to solve complex engineering problems-and bio-inspired data-driven methods and their applications in engineering control, optimization, and artificial intelligence. The proposed H-MODRL framework embodies core biomimetic principles: the Genetic Algorithm (GA) mimics Darwinian natural selection and genetic inheritance, the Sparrow Search Algorithm (SSA) abstracts the cooperative foraging and anti-predation behaviors of sparrow populations in nature, and the Arrhenius-based freshness-decay model captures the biochemical kinetics governing perishable biological products. By synergistically integrating these biological evolution principles, swarm intelligence, and deep learning, the framework tackles real-world logistics complexity in a manner directly inspired by living systems. This study presents a well-organized hybrid optimization framework (H-MODRL) that couples a three-stage hybrid evolutionary mechanism, synergistically integrating heuristic warm-start, evolutionary policy guidance, and deep reinforcement learning decision-making. First, an improved genetic algorithm combined with the earliest deadline first strategy constructs a feasible initial population satisfying hard time-window constraints. Second, a large neighborhood search-enhanced chaotic sparrow search algorithm builds a high-quality elite guidance set for policy learning. Third, a physics-based multi-objective proximal policy optimization model embedded with Arrhenius equation-derived freshness-decay kinetics performs online decision-making. Experiments demonstrate that pre-computed all-pairs shortest paths and an O(1) hash-based dynamic-disruption indexing mechanism support fast online replanning. On heterogeneous simulated terrains based on real Chinese geospatial data, H-MODRL outperforms state-of-the-art algorithms across four objectives-logistics cost, carbon emissions, terminal freshness, and delivery time-while exhibiting compact, low-variance performance distributions, thereby validating its engineering robustness and practical value in complex agricultural cold chain environments.
The magnitude and quality of adaptive immune responses are fundamentally influenced by the efficiency of antigen presentation. Traditional vaccine platforms, such as live-attenuated or inactivated pathogens, although immunogenic, often present safety concerns. Conversely, subunit vaccines, despite being safer, generally exhibit poor immunogenicity due to inadequate delivery of antigens to professional antigen-presenting cells (APCs). To address this issue, the development of innovative delivery systems has become a pivotal strategy to overcome significant biological barriers, including extracellular antigen degradation, suboptimal lymph node targeting, and inefficient cross-presentation necessary for CD8+ T cell activation. This review systematically explores recent advancements in delivery technologies aimed at enhancing antigen presentation, encompassing rationally engineered nanocarriers and sophisticated biomimetic platforms. We first examine how nanoparticle properties like size, surface charge, and ligand density affect intracellular trafficking and the transition from MHC-II to MHC-I cross-presentation. Then, we explore bioinspired systems such as extracellular vesicles, virus-like particles, and cell-membrane-coated nanoparticles that utilize natural biological traits for enhanced targeting and immune modulation. Additionally, we review new physical delivery methods like microneedle arrays and in situ electroporation for direct, minimally invasive antigen delivery to dendritic cells. Lastly, we discuss the potential of these platforms in personalized cancer vaccines and combination immunotherapies. By combining insights from materials science, immunology, and bioengineering, these next-generation delivery tools could enhance antigen presentation and transform precision vaccination and immune intervention.
Insects have long served as inspiration for robotic systems for various applications. Among them, bees have emerged as promising models for the discovery and implementation of strategies for aerial navigation in autonomous robots. This is motivated by bees' ability to perform robust visually guided behaviors to navigate complex spatial environments safely. The specific flight behaviors that these agile organisms perform when negotiating visual clutter are particularly relevant, as modern autonomous vehicles operate in similar cluttered environments, where parsimonious sensing and robust control are necessary. This review focuses on synthesizing discoveries from behavioral studies of bee flight in clutter, alongside complementary advances in implementing bio-inspired navigation strategies for robotic platforms. The sensory mechanisms and salient properties of crucial visual inputs, such as optical flow, which aid bees in navigating clutter, are discussed from a functional standpoint. Prevailing hypotheses on the strategies to accomplish key behaviors, including speed regulation, collision avoidance, and gap traversal, are described. To facilitate the translation to robotics, an outline of control laws for producing bee-like navigation behavior in representative cluttered environments is presented. Finally, we collate work from the past decade on bee-inspired robotic control and highlight avenues for future research to realize embodied intelligence in robotics. Through our analysis and survey, we identify open questions and research directions to deepen our understanding of the neuroethology of flight behavior in bees, as well as recent trends in the development of bio-inspired robotics research that operates at the interfaces of biology, engineering, and robotics.
To improve operational safety in confined spaces, this study proposes an intelligent safety monitoring framework that utilizes multimodal data from wearable devices. The framework comprises two core components: a human activity recognition (HAR) module and a bio-inspired adaptive multimodal decision fusion (BA-MDF) module. The HAR module processes accelerometer and gyroscope data through an enhanced FFT-LSTM architecture that integrates time- and frequency-domain features for real-time activity classification. The BA-MDF module, inspired by biological multisensory integration mechanisms-particularly the inverse effectiveness principle observed in the superior colliculus-evaluates contextual risk by adaptively fusing HAR outputs, heart rate variability, and geospatial constraints without additional computational overhead. Experimental testing demonstrated 92.4% overall HAR accuracy and 94.3% identification accuracy for emergency scenarios under a simulated sensor degradation environment. These results validate the framework's effectiveness in mitigating risks from anomalous events in visually constrained environments.
Aflatoxin B₁ (AFB₁), a potent natural carcinogen, poses a severe and widespread threat to global food safety and public health. To address this challenge, the rational design of robust artificial catalysts for efficient AFB₁ degradation represents a promising strategy. Inspired by the multicopper active center of natural laccase, we herein fabricated high-performance metalloenzyme mimics via a biomimetic interfacial co-assembly strategy for efficient AFB1 degradation. De novo designed peptides, incorporating histidine and cysteine as cooperative metal-binding motifs within a self-assembling LK peptide framework, underwent coordinative co-assembly with Cu²⁺ ions. Driven by synergistic metal-ligand interactions-primarily the imidazole groups of histidine and thiol groups of cysteine-the assembly process enabled the formation of well-defined, copper-enriched catalytic interfaces that accurately recapitulated the geometric architecture and electronic structure of laccase's active center, yielding peptide-copper colloidal nanoassemblies with remarkable laccase-mimicking activity. In comparison with natural laccase, the optimized metalloenzyme mimic displayed superior catalytic efficiency, as well as enhanced pH tolerance and thermal stability, enabling complete degradation of AFB₁ within 90 min under optimal conditions. The transformation products of AFB₁ showed markedly reduced cytotoxicity relative to parent mycotoxin. Importantly, the metalloenzyme mimic exhibited excellent practical performance, efficiently degrading AFB₁ in contaminated grain and nut samples and reducing residual concentrations to meet the strict safety limits set by the European Union. This work not only provides a potent biocatalyst for mycotoxin remediation but also elucidates a fundamental co-assembly pathway for engineering functional colloidal materials with tailored catalytic interfaces, offering broad implications for designing bio-inspired solutions in environmental and food chemistry.
The development of efficient catalysts for nitrogen conversion to ammonia is critical for a sustainable alternative to the energy-intensive Haber-Bosch process. Yet, rational catalyst design remains highly challenging, compounded by complex structure-function relationships within realistic conditions. Herein, we present an integrated computational framework combining quantum chemical calculations with 27 machine learning models to predict experimental catalytic metrics in metal-ligand complexes. The models are trained and validated on a large experimental database and demonstrate high predictive accuracy across multiple tasks. For classification, family 1 and family 2 catalysts achieved test accuracies up to 1. Regression models yield test R2 values of 0.91 and 0.88 for turnover frequency (TOF) and turnover number (TON) predictions in family 1, and 0.96 and 0.99 in family 2. Notably, the models accurately capture time-dependent variability of TOF and TON for new complexes, with predicted values closely matching experimental results. Moreover, strong transfer learning capability is observed for structurally distinct coordination architectures. Feature interpretation reveals clear design principles for optimal catalysts involving metal spin state, ligand geometry, charge distribution, and experimental conditions. Together, this study established an efficient and practical framework for discovery and inverse design of high-performance catalysts under realistic conditions, with broader relevance to electrocatalysis.
In this study, we propose a biologically inspired Self-Organizing Map-based Artificial Visual System (SOM-AVS) for unsupervised orientation detection in static images. By combining a biologically motivated front-end visual processing module with an unsupervised SOM layer, the proposed system captures key characteristics of early-stage visual processing, including localized orientation-sensitive responses and structured feature organization. The model enables the structure of distinct orientation-related representations without requiring labeled data, forming organized response patterns across the neural map. Experimental results demonstrate robustness under various conditions, including noise corruption, restricted perceptual experience, and limited training samples. Furthermore, the model shows adaptive behavior when exposed to new stimuli after initial training, indicating its potential to reflect experience-dependent adjustments in representation. These findings suggest that SOM-AVS provides a useful framework for exploring self-organization mechanisms in artificial visual systems and for developing biologically inspired perception models.
Magnetic soft robots are widely employed in micromanipulation applications because of their inherent biocompatibility, untethered actuation capabilities, and controllability. This study presents the fabrication and application of a U-shaped robotic gripper (U-SRG) and experimentally verifies the control performance of a magnetic control system for two-dimensional (2D) rotation, translational motion, deformation, and microsphere grasping. The magnetic component of the U-SRG was fabricated by doping polydimethylsiloxane with neodymium-iron-boron (NdFeB) powder, followed by molding via post-treatment with Ecoflex-30 elastic silicone. The magnetic field generated by the electromagnetic coils of a magnetic control system can be conveniently, quickly, and precisely regulated using computers. Moreover, a uniform magnetic field could be precisely steered within a 2D plane, and the deformation magnitude of the U-SRG could be tuned by adjusting the intensity of the uniform magnetic field. The planar motion of the U-SRG was controlled by a synthetic magnetic field, and its speed was adjusted according to the magnitude of the magnetic field gradient. The magnitude and direction of the magnetic field required for each segment of the U-SRG path can be preset using computer software, enabling the U-SRG to precisely grasp and release the microspheres. To improve the micro-object grasping efficiency of the U-SRG, a crab-inspired gripper, denoted as U-SRGs, was developed by optimizing the design of the U-SRG. The bioinspired design does not directly replicate the external morphology of crab claws; instead, it draws inspiration from the functional differentiation of paired crab chelae, which can cooperate while exhibiting different grasping roles. This biological principle was translated into an asymmetric magnetic design strategy for the U-SRGs. By asymmetrically doping the left and right fingers of the U-SRGs with NdFeB powder, the gripper achieved differentiated clamping deformation under a uniform magnetic field. Owing to this capability, U-SRGs can realize size-based screening, manipulation, and targeted delivery of micro-objects.
Biological thin-sheet systems, including leaves, insect wings, and flowering organs, achieve adaptive deformation through distributed compliance, segmentation, curvature, and controlled opening. Kirigami offers a bio-inspired route for translating such deformation logics into programmable thin-sheet surfaces; however, the geometric parameters that most strongly influence elastic displacement remain insufficiently quantified, especially across different loading regimes. This study investigates Bio-Inspired Regime-Dependent Parameter Selection in Parametric Kirigami through twenty-five laser-cut specimens spanning five boundary shapes and three thermoplastic substrates. Specimens were tested under two contrasting regimes: quasi-static tensile loading and gravity-drape loading. Elastic displacement was measured under eight-point boundary fixation and analyzed using regime-separated Pearson correlations, Bonferroni-corrected significance testing (α/18 = 0.0028), and shape-controlled partial correlations. Under tensile loading, the Number of Offsets (r = 0.807), Segments per Offset (r = -0.603), and outer-boundary void perimeter (r = 0.621) showed the strongest Bonferroni-robust associations with displacement. Under gravity-drape loading, effects were weaker and more curvature-sensitive, indicating that parameter relevance is not universal but regime-dependent. Within the tested parametric design space, the study provides an experimentally grounded basis for selecting Kirigami geometric parameters in thin-sheet structures whose adaptive deformation logic is analogous to compliant systems found in nature.
Aortic stenosis is predominantly treated through transcatheter bioprosthetic heart valve implantation. However, the materials used in these devices are prone to premature failure. Polymer heart valves provide an alternative to current commercial devices, offering materials with greater durability and customisation through fibre reinforcement. Given the wide range of available materials and structures, there is a need for a systematic and efficient approach to designing and optimising novel bioinspired polymeric leaflets. This work presents a framework that employs computational modelling and Design of Experiments (DOE) tools to optimise bioinspired, 3D-printed, fibre-reinforced polymer leaflets made using melt electrowriting (MEW). Here, finite element (FE) models are created to represent MEW fibre-reinforced polymer leaflets for application in a transcatheter aortic heart valve. The behaviour of this valve under physiological loading conditions is modelled to predict valve performance and leaflet material response. These models were first used to investigate the impact of fibre orientation on valve performance and leaflet response, thereby demonstrating the benefits of a bioinspired fibre reinforcement structure. Using a DOE approach, the structural combination of MEW fibre reinforcement and an elastomeric matrix was optimised to improve valve performance and reduce leaflet stress and strain. Overall, the framework offers an efficient and versatile methodology for optimising fibre-reinforced polymer leaflets using an in silico approach, thereby reducing the need for physical prototyping and testing of these next-generation devices during early product development.
Biomimetic spider silk achieves remarkable functionalities through hierarchical architectures with highly oriented crystalline domains, offering potential across multiple disciplines. However, achieving uniform alignment and spatial control of nanocrystalline domains remains a critical challenge, limiting the realization of structure-derived optical and mechanical functionalities in bioinspired systems. Here, we develop an ultrastrong, transparent photonic hydrogel composed of cellulose nanocrystals (CNCs), wherein a programmable five-stage stretching-pause process enables precise alignment of CNC domains without sacrificing their intrinsic chirality-unattainable in conventional flexible polymers. This strategy facilitates uniform nanocrystal reorientation (orientation factor = 0.91) and transforms the porous network into aligned nanofibril bundles, yielding optical transparency (>90%) with anisotropic polarization responses, superior mechanical strength (61.6 MPa), toughness (251.8 MJ·m-3), and fatigue resistance (226.7 kJ·m-2). The flexible hydrogel resists creasing and serves as a sustainable scattering polarizer for programmable polarized displays and secure information encryption, providing a versatile platform for advanced optical and electronic applications.
Osteoarthritis (OA) is driven by a self-perpetuating cycle of cartilage wear, persistent inflammation, and lubrication dysfunction. Inspired by the natural superlubrication and piezoelectricity of healthy cartilage, we designed injectable hydrogel microspheres (HMBTLCs) that break this vicious cycle through a triple synergistic action of lubrication, anti-inflammation, and pro-regeneration. These microspheres are fabricated via a facile microfluidic strategy, integrating curcumin-loaded liposomes and barium titanate (BaTiO3, BT) nanoparticles within a single platform. The liposomes form a self-renewing boundary lubrication layer on the microsphere surface, achieving an ultralow coefficient of friction of 0.022, and provide sustained release of curcumin for over 28 days to scavenge inflammatory factors, thereby ameliorating the OA microenvironment. Concurrently, the embedded BT nanoparticles convert physiological joint loading into endogenous electrical signals (≈20 mV under 3 N load) to promote chondrogenesis. Critically, transcriptomic analysis reveals a cooperative mechanism: curcumin and piezoelectric stimulation not only synergistically upregulate chondrogenic markers (e.g., ACAN) and downregulate the catabolic enzyme MMP13 through distinct pathways, but also converge on shared anti-inflammatory signaling pathways (e.g., TNF-α, NF-κB, IL-17) to promote macrophage M2 polarization (CD206/CD86 ratio increased by 7.7-fold). By orchestrating these chemical and physical cues, HMBTLCs interrupt the pathological OA cycle under both cellular and animal conditions, reducing the OARSI score by 79.31% in a rat OA model, demonstrating their therapeutic potential for OA while also serving as a model for biomimetic material design.
Superhydrophobic material design has predominantly relied on direct structural replication of singular natural archetypes, such as the lotus leaf. While this biomimetic strategy has driven significant progress, it fundamentally fails to translate the dynamic droplet super-repellency into universally applicable predictive models. Here, we report a biomimetic laboratory toolkit designed to overcome the variability of natural leaves and establish a standardized paradigm for herbicide formulation screening. By integrating the high-ridge architecture of C3 grasses with the dense-groove networks of C4 grasses, we engineered a structural test strip that serves as a conservative worst-case benchmark. This engineered platform, combined with kinetic contact tension (KCT) analysis, enables the precise prediction of droplet deposition kinetics, facilitating high-throughput adjuvant screening prior to field trials.
Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder worldwide, and its early diagnosis remains a major challenge due to reliance on subjective clinical assessments. This study proposes a bio-inspired computational framework for automatic PD detection that draws explicit architectural inspiration from two biological systems: the hierarchical tonotopic organization of the human auditory cortex, which motivates the design of a 1D Convolutional Neural Network (CNN) for vocal biomarker analysis, and the basal ganglia-cerebellar motor control circuit, which motivates the selection and design of features extracted from Archimedean spiral drawing tasks. Unlike previous studies that apply standard machine learning techniques without grounding architectural choices in biological mechanisms, the proposed framework establishes a direct mapping between neural processing pathways and model design decisions. A Support Vector Machine (SVM) classifier evaluated on the Kaggle vocal dataset achieved 87% test accuracy with no overfitting, outperforming AdaBoost, Random Forest, KNN, XGBoost, and Decision Trees in terms of generalization. The 1D CNN applied to UCI spiral drawing data achieved 85% test accuracy, with overfitting behavior addressed through architectural regularization strategies including early stopping. A conceptual multimodal fusion architecture integrating both modalities is proposed as a direction for future experimental validation; it was not implemented or experimentally validated within the present study. The primary novelty of the framework resides in this explicit biomimetic grounding, which distinguishes it from existing performance-driven approaches. Results confirm that biologically grounded computational models constitute promising objective decision-support tools for early PD diagnosis.
To address the challenges of high-dimensional nonlinearity, multimodal landscapes, and stringent constraints prevalent in modern engineering design, traditional meta-heuristic algorithms often suffer from a loss of population diversity and premature convergence. Inspired by the social collaborative predation and collective information interaction behaviors of P. prominens (jumping spiders), this study proposes a novel bio-inspired meta-heuristic optimization algorithm, termed the Experience Exchange Strategy-Enhanced Philoponella Prominens Optimization (EESPPO). The proposed EESPPO integrates an Experience Exchange Strategy framework to reshape the search dynamics of the population through three progressive evolutionary stages: (1) In the Experience Scarcity (ESC) stage, the algorithm focuses on the construction and dynamic maintenance of an experience library to ensure the effective preservation of high-quality historical information; (2) In the Experience Crossover (ECR) stage, a random guidance vector generation mechanism is introduced to significantly enhance population behavioral diversity and the capability to escape local optima; (3) In the Experience Sharing (ESH) stage, an adaptive fusion update strategy is employed to achieve efficient information interaction and co-evolution among individuals. These three stages operate synergistically within the optimization cycle to establish a dynamic balance between global exploration and local exploitation, effectively overcoming the inherent defects of premature convergence in traditional meta-heuristics. Extensive empirical analysis based on the CEC2017 benchmark functions confirms that EESPPO comprehensively outperforms 12 existing advanced algorithms (including PPO, HSO, SGA, PSO, FLO, DE, HO, WOA, KEO, GWO, FDB-AGSK, and IVYPSO) in terms of convergence accuracy and robustness. Furthermore, the application of EESPPO to four challenging engineering design problems confirms its superiority. The experimental results validate the high precision and feasibility of EESPPO in solving complex constrained engineering problems.
Hysteresis is normally unavoidable in hydrogels under complex external loading conditions due to the intermolecular friction, which usually leads to fatigue. Here, we fabricate a sarcomere-inspired double-network hydrogel made from polyacrylamide, alginate and phytic acid, whose hysteresis can be effectively regulated by preloading. Particularly, due to the synergy of micellization, fibrillation and micro-lubrication, the as-prepared hydrogel displays an ultralow hysteresis (≤0.02%) after it experiences a pre-tensile process at a specific amplitude and strain rate, or even possesses negative hysteresis in the case of low tensile amplitudes or high strain rates. Interestingly, smart responses of the developed hydrogel to cyclic tensile loadingare similar to the mechanical behaviors of sarcomeres in vivo. Likewise, the derived hydrogel with ultralow hysteresis performs reliably even at temperatures as low as -20 °C. The ultralow hysteresis presented by the biomimetic hydrogel with ultralow hysteresis makes it suitable for many engineering fields like electrical sensing with superior reliability (the corresponding electrical signal (ΔR/R0) is stable even after 1000 stretching-unstretching cycles). Moreover, the design strategy of hydrogels with programmable hysteresis provides an innovative methodology for the future development of smart high-performance hydrogels.
To enhance the thermal management of lithium-ion batteries in new-energy vehicles, various bio-inspired liquid-cooled plate channel designs were investigated to improve hotspot dissipation within the laminar flow regime. A series of three-dimensional numerical simulations were conducted to compare leaf vein-, tree branch-, honeycomb-, and spider web-inspired channels, followed by further optimization to improve thermohydraulic performance. The selected optimized bio-inspired channels were subsequently evaluated against conventional structures. Simulation results indicate that the honeycomb-inspired liquid-cooled plate channel achieved the best performance, followed by the tree branch- and spider web-inspired channels, which exhibited comparable thermohydraulic performance. The leaf vein-inspired channel demonstrated the lowest performance. The key design element for enhanced heat dissipation is the inclusion of longitudinal branch channels, which minimize flow zones with near-zero velocity and effectively mitigate local hotspots. Furthermore, the combination of longitudinal and inclined branch channels can redirect flow direction and enhance fluid mixing. Compared with the conventional channel widely adopted in existing studies, within the Reynolds number range of 260 to 920, the optimized honeycomb-inspired liquid-cooled plate channel achieves a 44.0-49.3% increase in Nusselt number and an 81% enhancement in comprehensive performance metric. Concurrently, thermal resistance is diminished by 2.6-9.2%, and pumping power is reduced by 50.0-56.8%.
Conventional thrust vector control nozzles are severely constrained by a single-pivot deflection paradigm, which induces asymmetric shock reflections and adverse boundary layer separation at large angles. Multi-segmented serial configurations offer a promising alternative to overcome these limitations by distributing the total deflection across multiple joint interfaces, thereby achieving large terminal angles and smooth flow-path curvatures. To realize such a configuration, this study draws inspiration from the abdominal bending mechanism of the damselfly Ischnura elegans during mating wheel formation. Real-time video recording and morphological characterizations identified abdominal segments VI and VII as critical for high-amplitude bending under load. Finite element analysis under muscular actuation elucidated the biomechanical synergy, which was rigorously verified through mesh convergence and material property sensitivity checks. Inspired by this biological system, a multi-segmented nozzle configuration incorporating discrete elastic elements and a centralized cable-driven layout was designed and evaluated using multibody dynamics and computational fluid dynamics. The nozzle achieved a continuous 61.20° deflection within 8 s under subsonic exhaust conditions, successfully stabilizing periodic supersonic shock structures and completely suppressing adverse boundary layer separation. These findings turn biological bending into a thrust vectoring method, giving insights for next-generation agile aerospace propulsion systems.
Marine invertebrates exhibit diverse thermoregulatory capabilities enabled by hierarchical architectures, porous skeletal frameworks, and adaptive interfaces. These biological features provide engineering cues for controlling heat conduction, convection, and radiation, particularly when lightweight and multifunctional thermal designs are required. This review surveys marine-invertebrate-inspired thermal management from an engineering perspective and synthesizes biological structure-function relationships into transferable design concepts. Literature was collected from Scopus, Web of Science, and Google Scholar. Studies were included if they (i) explicitly referenced marine invertebrate morphology, structural organization, interfacial behavior, or adaptive mechanisms and (ii) quantitatively reported thermal metrics such as thermal conductivity, heat capacity/latent heat, heat dissipation performance, or temperature modulation. To maintain biological scope while enabling cross-comparison, the review is organized across major marine invertebrate phyla frequently used in bioinspired engineering-Mollusca, Porifera, Cnidaria, Echinodermata, and Arthropoda-and the engineering literature is classified into three categories: (A) bio-inspired functional materials for thermal transport or optical-thermal control; (B) bio-inspired structural architectures that guide heat flow via hierarchical or porous geometries; and (C) integrated thermal management systems that couple multiple mechanisms at the device or system scale. Across these categories, the reviewed studies demonstrate promising routes toward electronics cooling and aerospace thermal protection. Remaining challenges include scalable fabrication over large areas, flow uniformity in microchannel-based platforms, and long-term reliability under combined pressure, salinity, and thermal cycling.
Insect-Inspired flapping-wing micro aerial vehicles (FWMAVs) have attracted significant attention due to their unique advantages in agility, manoeuvrability, low noise, and adaptability to cluttered environments. Over the past two decades, research in this field has progressed from early conceptual demonstrations to more advanced platforms capable of hovering, rapid manoeuvres and limited autonomous flight. This review summarizes the historical development of FWMAVs, highlights key unsteady aerodynamic mechanisms such as the leading-edge vortex, wake capture, clap-and-fling, rotational lift and added-mass effects, and analyses their roles in enabling lift enhancement under low Reynolds number conditions. Actuation approaches including motor-driven, piezoelectric, electromagnetic and emerging soft-material-based systems are examined, together with structural innovations in wing configurations such as two-wing, four-wing, X-wing, and multi-wing architectures. Control strategies for tailless vehicles, including wing-kinematics modulation, attitude feedback control and onboard sensing, are systematically reviewed. Despite significant progress, current FWMAVs still face major challenges in energy efficiency, endurance, lack of adaptability to different environments, environmental robustness and material limitations. Future development will require integration across disciplines such as smart materials, high-efficiency power systems, micro-fabrication and advanced control algorithms to achieve truly autonomous, robust, and long-range bio-inspired flight.